基于PU学习的链接预测方法

Link Prediction Method Based on PU Learning

基于分类的链接预测方法中,由于链接未知节点对的大规模性与不确定性,选择可靠负例成为构造链接预测分类器的难点问题.为此,文中提出基于正例和无标识样本(PU)学习的链接预测方法.首先,提取节点对的拓扑信息以构造样本集.再利用社区结构确定候选负例的分布,基于分布进行多次欠采样,获得多个候选负例子集,集成多个负例集与正例集中构建的分类器选择可靠负例.最后基于正例与可靠负例构造链接预测分类器.在4个网络数据集上的实验表明文中方法预测结果较优.

In classification-based link prediction methods, it is difficult to choose reliable negative examples to construct the link prediction classifier due to the large-scale and uncertainty of the unknown node pairs.Therefore, a link prediction method based on positive and unlabeled(PU) learning is proposed.Firstly, topological information of node pairs is extracted to construct example sets.Secondly, distribution of candidate negative examples is determined by community structure, and several candidate negative example sets are obtained through multiple under-sampling based on the distribution.Then, the classifiers constructed from multiple negative example sets and positive example sets are integrated to select reliable negative examples.Finally, the link prediction classifier is constructed based on positive examples and reliable negative examples.Experiments on four datasets show that the proposed link prediction method produces better prediction results than other related methods.